Tunable Algorithmic Segments

ABSTRACT

Tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth. Tuning parameters designated for the look-alike model are ascertained and the look-alike model is built based on the target audience definition and the tuning parameters. The tuning parameters may include at least a setting selectable to control reach versus accuracy for the look-alike model. Segment data indicative of market segments generated according to the look-alike model may then be exposed for manipulation by a client. The manipulation may include selectable control over the tuning parameters to generate different look-alike groups from the segment data.

BACKGROUND

As consumer interaction with online resources (e.g., use of webservices, e-commerce, browsing activity, etc.) has grown digitalmarketing too has becoming increasingly more common. Generally, digitalmarketers seek to deliver offers for products, services, and content toconsumer audiences who will find the offers favorable and have highprobability of responding to the offers. One challenge faced by digitalmarketers is finding “look-alike” groups that have traits comparable toknown traits of existing target audiences so as to facilitate expansionof existing marketing campaigns to the look-alike groups.

Traditionally, demographic and behavioral data (e.g., audience data) maybe collected and analyzed to model known target groups and identifypotential new look-alike consumers. Due in part to the amount ofaudience data available for online consumers, though, the look-alikeanalysis may be complex and time consuming. As, such timely andeffective manual analysis may be impractical. Moreover, digitalmarketers are traditionally provided little or no control over automatedtools that purport to provide look-alike analysis. Rather, existinganalysis tools are black-box solutions that output fixed audiencesegments without opportunity for digital marketers to adjust theanalysis based on their intuition and experience. Accordingly, adequatemechanisms do not currently exist to identify and target offers tolook-alike consumers that have characteristics similar to a known group.

SUMMARY

Tunable algorithmic segment techniques are described. In one or moreimplementations, a target audience definition is obtained that is inputto initiate creation of a look-alike model. The target audiencedefinition indicates traits associated with a baseline segment ofconsumers who have interacted with online resources in a designatedmanner, such as by buying a product, visiting a website, using aservice, and so forth. Tuning parameters designated for the look-alikemodel are ascertained and the look-alike model is built based on thetarget audience definition and the tuning parameters. The tuningparameters may include at least a setting selectable to control reachversus accuracy for the look-alike model. Segment data indicative ofmarket segments generated according to the look-alike model may then beexposed for manipulation by a client. The manipulation may includeselectable control over the tuning parameters to generate differentlook-alike groups from the segment data.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different instances in thedescription and the figures may indicate similar or identical items.Entities represented in the figures may be indicative of one or moreentities and thus reference may be made interchangeably to single orplural forms of the entities in the following discussion.

FIG. 1 is an illustration of an example operating environment that isoperable to employ techniques for tunable algorithmic segments.

FIG. 2 is a diagram depicting some example scenarios for producingsegments in one or more implementations.

FIG. 3 is a flow diagram depicting an example procedure to generatetunable algorithmic segments.

FIG. 4 is a flow diagram depicting an example procedure to modifysegment data for a look-alike model in one or more implementations.

FIG. 5 is a diagram depicting an example user interface for interactionwith segments in accordance with one or more implementations.

FIG. 6 is a diagram depicting an example user interface for defining alook-alike model in accordance with one or more implementations.

FIG. 7 is a diagram depicting an example user interface for showingselections to define a look-alike model in accordance with one or moreimplementations.

FIG. 8 is a diagram depicting an example user interface for interactionwith a look-alike model in accordance with one or more implementations.

FIG. 9 is a diagram depicting an example user interface for adjustmentof reach versus accuracy in accordance with one or more implementations.

FIG. 10 illustrates an example system that can be employed to implementaspects of the techniques described herein.

DETAILED DESCRIPTION

Overview

Tunable algorithmic segment techniques are described. In one or moreimplementations, a target audience definition is obtained that is inputto initiate creation of a look-alike model. The target audiencedefinition indicates traits associated with a baseline group ofconsumers who have interacted with online resources in a designatedmanner, such as by buying a product, visiting a website, using aservice, and so forth. Tuning parameters designated for the look-alikemodel are ascertained and the look-alike model is built based on thetarget audience definition and the tuning parameters. The tuningparameters may include at least a setting selectable to control reachversus accuracy for the look-alike model. Segment data indicative ofmarket segments generated according to the look-alike model may then beexposed for manipulation by a client. The manipulation may includeselectable control over the tuning parameters to generate differentlook-alike groups from the segment data.

In the following discussion, an example environment is first describedthat may implement the techniques described herein. Example detailsregarding tunable algorithmic segments techniques are then discussed.This discussion of example details includes separate sub-sections forexample procedures, segmentation algorithms, and example userinterfaces. Lastly, an example system and components of the system arediscussed that may be employed to implement various techniques describedherein.

Example Environment

FIG. 1 is an illustration of an environment 100 in an exampleimplementation that is operable to employ techniques described herein.The illustrated environment 100 includes a client device 102, one ormore data sources 104, and a service provider 106 that arecommunicatively coupled via a network 108. The client device 102, datasources 104, and service provider 106 may each be implemented by one ormore computing devices and also may be representative of one or moreentities.

A computing device may be configured in a variety of ways. For example,a computing device may be configured as a computer that is capable ofcommunicating over the network 108, such as a desktop computer, a mobilestation, an entertainment appliance, a set-top box communicativelycoupled to a display device, a wireless phone, a game console, and soforth. Thus, the computing device may range from full resource deviceswith substantial memory and processor resources (e.g., personalcomputers, game consoles) to a low-resource device with limited memoryand/or processing resources (e.g., traditional set-top boxes, hand-heldgame consoles). Additionally, although a single computing device isshown in some instances, the computing device may be representative of aplurality of different devices, such as multiple servers of the serviceprovider 106 utilized by a business to perform operations, and so on.Further examples of computing systems and devices suitable to implementtechniques described herein are described below in relation to FIG. 10.

Although the network 108 is illustrated as the Internet, the network mayassume a wide variety of configurations. For example, the network 108may include a wide area network (WAN), a local area network (LAN), awireless network, a public telephone network, an intranet, and so on.Further, although a single network 108 is shown, the network 108 may beconfigured to include multiple networks.

The client device 102 is further illustrated as including an operatingsystem 110. The operating system 110 is configured to abstractunderlying functionality of the underlying device to applications 112that are executable on the client device 102. For example, the operatingsystem 110 may abstract processing, memory, network, and/or displayfunctionality such that the applications 112 may be written withoutknowing “how” this underlying functionality is implemented. Theapplication 112, for instance, may provide data to the operating system110 to be rendered and displayed by a display device as illustratedwithout understanding how this rendering will be performed. A variety ofapplications 112 typically associated with client devices arecontemplated including, but not limited to, a document reader, amulti-media player, image editing software, a productivity suite thatintegrates multiple office productivity modules, games, and so forth. Asspecific example of applications 112, the client device 102 is alsoillustrated as including a marketing module 114 and a browser 116.

The marketing module 114 may be configured in various ways to implementclient side aspects of techniques for tunable algorithmic segmentsdescribed herein. As illustrated, the marketing module 114 may beprovided as a standalone application that may enable digital marketersto manage marketing campaigns, audience data, market segments, and soforth. In general, this includes audience data collection, analysis ofaudience data to ascertain market segments of consumers having selectedtraits, creation of offers for products, services, and/or content, anddistribution of the offers. The marketing module 114 may facilitateinteraction with a marketing service 118 provided by the serviceprovider 106 over the network. Thus, the marketing module 114 mayrepresent a thin client web-based application or a web-enabled desktopapplication through which a digital marketer may access a marketingaccount with the marketing service 118 and interact with correspondingdata. In addition or alternatively, the techniques described herein maybe implemented by way of the browser 116, which may be configured toaccess the marketing service 118 over the network 108.

As noted, the service provider 106 may provide a marketing service 118as depicted in FIG. 1. The marketing service 118 is representative of anintegrated digital marketing platform configured to provide a suite ofdigital marketing tools including but not limited to consumer datacollection and analytics, social media management, digital advertising,audience targeting, and/or web experience management, to name a fewexamples. The marketing service 118 also enables generation of tunablealgorithmic segments as described herein. Various digital marketingtools may be made accessible via webpages or other user interfaces 120that may be accessed and rendered by a client device 102 ascorresponding user interfaces 120′. Some example user interfaces toimplement aspects of the techniques for tunable algorithmic segmentsdescribed herein are discussed below in relation to FIGS. 5-9. Themarketing service 118 may be implemented in the “cloud” as a serviceaccessible over the network as illustrated, by one or more distributedcomponents in a client-server environment, as a locally deployedenterprise platform, and/or in another suitable manner.

In accordance with tunable algorithmic segment techniques describedabove and below, the marketing service 118 may include or otherwise makeuse of a data collection module 122 and an analytics module 124 that areconfigured to obtain and manipulate audience data 126 from the datasources 104. In particular, the data collection module 122 representsfunctionality operable to collect, access, and/or make use of audiencedata 126 regarding consumer traits including characteristics (e.g., age,sex, location, affiliations, etc.) and behaviors (e.g., browsing habits,favorites, purchase history, preferences, account activity, etc.) fromthe various data sources 104. The data sources may include first partydatabases of a particular marketer, data collected by the serviceprovider 106, and/or third-party data services provided by otherentities. The data collection module 122 may collect and store audiencedata 126 on behalf of digital marketers. For example, audience data 126may be collected based on visitors to a company website, through onlinesurveys, as part of e-commerce transactions, based on account sign-ups,and so forth.

The analytics module 124 represents functionality operable to performvarious analysis of audience data 126 to assist digital marketers inmaking marketing decisions, creating and managing campaigns, generatingreports, delivering ads/offers, and so forth. By way of example and notlimitation, the analytics module 124 includes functionality to performlook-alike analysis as described above and below.

Generally speaking, look-alike analysis is performed to find individualswithin a general audience (as described by audience data 126 fromselected sources) that match traits of a known, baseline group ofindividuals that may have behaved in a designated manner and/or haveselected traits. Here, the known group is considered the target audiencesegment for a particular marketing campaign, and the look-alike analysisseeks to discover individuals to which the marketing campaign may beexpanded with a high probability of success. Typically, the known grouphas engaged in some prior activities in relation to the marketer seekingto expand the campaign. In other words, a target audience segment thatpossesses known traits is used to analyze audience data 126 and find amatching, look-alike segment of the general audience that shares atleast some of the known behaviors and traits. The identified look-alikesegment that is generated may include a list of individual useridentities for members of the general audience that match the targetsegment based on the analysis. In one approach, the individual useridentities are provided anonymously to the digital marketer to protectprivacy rights. However, the information regarding the look-alikesegment is sufficient to enable the digital marketer to deliver offersfor products, services, and/or content to the identified individualsthrough the marketing service or otherwise.

To further illustrate, consider now FIG. 2 which depicts generally at200 a diagram that represents an example process for look-alike analysisin accordance with the described techniques. In particular, theanalytics module 124 in this example includes or makes use of one ormore segmentation algorithms 202 that may be applied to identifylook-alike segments from suitable inputs. In this example, the inputsinclude audience data 126, a target audience definition 204, and tuningparameters 206. A selected segmentation algorithm 202 may be applied tothese inputs to produce segment data 208 as shown in FIG. 2. Here, thesegment data 208 may encompass one or more segments (e.g., groupings ofusers having common traits) generated through the analysis as well asunderlying data, weights, parameters, and scores used to produce thesegments. Providing both the segments and the underlying informationprovides transparency to the analysis and enables subsequent tuning bythe digital marketer as described herein. Some details regarding examplesegmentations algorithms 202 are described in a section below entitled“Segmentation Algorithms.”

The audience data 126 may include data from one or more data sources 104as previously mentioned. In an implementation, a digital marketer may begiven an option to select from among multiple available data sources.The target audience definition 204 is configured to indicate the traitsof the known group selected to provide a baseline for the look-alikeanalysis. For instance, the marketing service 118 may provide userinterfaces 120 through which a digital marketer may select an existingsegment and/or particular traits to create a target audience definition204. The target audience definition is then used as a basis fordiscovering a matching segment of individuals having similarcharacteristics and behaviors (e.g., a look-alike segment) from theaudience data 126.

The analytics module 124 may also be configured to employ tuningparameters 206. The tuning parameters 206 enable a digital marketer toexert control over the output of the analysis and form differentlook-alike segments in different scenarios and/or to achieve differentgoals. Thus, the automated power of algorithmic-based analysis may besupplemented through various tuning parameters 206 with the intuitionand experience of the digital marketer. The result is timely (e.g.,relatively fast computation) and effective look-alike analysis thatcombines a segmentation algorithm 202 with user configurable tuningparameters 206. This is in contrast to traditional black-box solutionswhich merely return static results without an opportunity for tuning.

The tuning parameters 206 may include at least a setting selectable tocontrol reach versus accuracy for the look-alike model. Reach is ameasure of how many individual user identities for members of thegeneral audience are included in the segments generated. Accuracy (alsoreferred to as similarity) is a measure of how closely individualsincluded in a segment match the target segment. Generally, reach (e.g.,the number of matching individuals) decreases as accuracy (e.g.,closeness of the match) increases. Thus, tuning parameters may includeone or more reach-accuracy parameters that enable a digital marketer tospecify a particular reach goal or accuracy goal and/or to select valuesthat provide an acceptable balance between reach and accuracy. Thetuning parameters 206 may also include other factors including but notlimited to weighting factors to control the contribution of particulartraits, a time period indicative of an interval for processing audiencedata, data source selections, weights to control the contribution ofdifferent data sources, addition of customized traits, removal ofselected traits, segmentation algorithm selections, model run frequency(e.g., daily, weekly, monthly, etc.) and so forth. Details regardingthese and other aspects of techniques for tunable algorithmic segmentsare discussed in relation to the following figures.

Having considered an example environment, consider now a discussion ofsome example tunable algorithmic segment details in accordance with oneor more implementations.

Tunable Algorithmic Segment Details

This section describes details regarding tunable algorithmic segments inaccordance with one or more implementations. As mentioned, a marketingservice 118 may be implemented to provide look-alike analysis thatcombines automated segmentation algorithms with user configurable tuningto generate look-alike segments in a timely and effective manner. In thediscussion that follows, example procedures are first discussed followedby a description of some example segmentation algorithms. Example userinterfaces that may be employed to implement aspects of the techniquesfor tunable algorithmic segments are discussed thereafter.

Example Procedures

The following discussion describes example procedures that may beimplemented utilizing the previously described systems and devices.Aspects of each of the procedures may be implemented in hardware,firmware, or software, or a combination thereof. The procedures areshown as a set of blocks that specify operations performed by one ormore devices and are not necessarily limited to the orders shown forperforming the operations by the respective blocks. In portions of thefollowing discussion, reference may be made to the environment 100 ofFIG. 1 and the diagram 200 of FIG. 2. In at least some embodiments, theprocedures may be performed by a suitably configured computingdevice(s), such one or more server devices associated with the serviceprovider 106 that implement a marketing service 118 and/or a clientdevice 102 of FIG. 1 that includes a suitable marketing module 114 orbrowser 116 to implement the described techniques.

FIG. 3 is a flow diagram depicting a procedure 300 in which a look-alikemodel is built. A target audience definition is obtained that is inputby a client for creation of a look-alike model (block 302). For example,a digital marketer may interact with a marketing service 118 to specifya target audience definition 204. As previously described, the targetaudience definition 204 may indicate traits (e.g., characteristics andbehaviors) for a known, baseline group of individuals (e.g., the targetsegment). A target audience definition 204 to initiate a look-alikeanalysis may be input in any suitable way. One way this may occur isthrough user interfaces 120 exposed by the marketing service 118.Through such interfaces, a marketer may be presented lists from whichthe marketer may select an existing segment and/or existing traits touse as a basis for look-alike analysis. In addition or alternatively, aform with various controls and fields operable to input the targetaudience definition may be exposed. Various other techniques are alsocontemplated, examples of which are discussed below in relation toexample user interfaces.

Tuning parameters are ascertained that are designated for the look-alikemodel (block 304). Then, the look-alike model is built based upon thetarget audience definition and the tuning parameters (block 306). Asmentioned the tuning parameters 206 enable the digital marketer (orother client) to control the model and segments that are generated. Insome scenarios, tuning parameters 206 may be specified as part of thetarget audience definition 204. Default parameters may also be specifiedin advanced by a developer or configured by a client in a set-up phase.If tuning parameters 206 are not specified initially, the defaultparameters may be applied. Thereafter, the tuning parameters may beadjusted to cause a corresponding change in the segment data 208 for amodel and/or the look-alike segments that are generated. Thus, the samemodel may be used to selectively generate different look-alike segments.Such selective creation of segments may be based on campaign goals suchas a reach goal, accuracy goal, a reach-accuracy trade-off, and soforth.

Segment data generated according to the look-alike model is exposed formanipulation by the client (block 308). Here, a selected segmentationalgorithm 202 may applied to generate segment data 208 according to themodel. The look-alike model incorporates the target audience definitionand tuning parameters such as a time period for analysis, selected datasources, a specified algorithm, reach-accuracy settings, a designatedmodel run frequency, and so forth. The marketing service 118 may beconfigured to save the model in association with the client/marketer.The marketing service 118 may then automatically run the model at thespecified frequency and output the resultant segment data for use by theclient/marketer.

In an implementation, segment data 208 and/or a notification thatsegment data has been generated may be communicated to the client usingvarious messaging techniques including but not limited to email, instantmessaging, voicemail, and so forth. The segment data 208 may be suppliedin the form of a report that may include lists of individuals, graphicrepresentations of the model, links to access the data/model online,and/or other relevant information. In addition or alternatively, themarketing service 118 may provide links and/or online access to themodel and segment data 208 for manipulation by the client. For example,the client may access the marketing service 118 from a browser 116 orclient marketing module 114 and log into an account. The client may thenbe provided access to view the model and corresponding segment data 208,make changes to the tuning parameters 206, modify settings for themodel, run the model on demand, select a different algorithm, changedata sources, and otherwise interact with the look-alike model andcorresponding segment data. Additional details regarding interactionswith a look-alike model to modify segment data are described in relationto the following example procedure.

FIG. 4 is a flow diagram depicting a procedure 400 in which segment datais modified based on input obtained via one or more tuning parametercontrols. A representation of segment data is output that is generatedbased on a look-alike model (block 402). For example, one or more userinterfaces 120 (e.g., web pages or web accessible pages for a clientapplication) may be output to enable various interactions with alook-alike model and corresponding segment data as described above. Inaddition or alternatively, the segment data 208 may be provided by wayof one or more reports that may be generated automatically or on demandvia the marketing service 118. The representation may include anindication of tuning parameters used to build the look-alike model. Therepresentation may also show or provide access to segment data 208including scoring data for different individuals; groups and/or list ofindividuals assigned into different segments; an indication of traitsand/or a baseline segment used to produce the segment data 208; and soforth. Further, the representation may include various graphicalrepresentations of the segment data 208 one example of which is a reachversus accuracy chart as described in further detail in the example userinterface section below.

One or more controls are exposed that are operable to enable adjustmentsto tuning parameters to modify the segment data (block 404). Variousdifferent controls are contemplated. For example, input boxes may beprovided to directly input tuning parameter values such as a reach goalor an accuracy goal for the model. In addition or alternatively, radiocontrols may be provided to set values for reach-accuracy, select asegmentation algorithm, designate a time period for analysis, choosedata sources, and so forth. Another example is a slider control. Forexample, a slider control may be exposed that is operable to set anaccuracy goal and/or set a balance between reach and accuracy. A varietyof other controls suitable to adjust tuning parameters 206 and causecorresponding changes in segment data 208 are also contemplated.

Input is obtained via the one or more controls to change at least one ofthe tuning parameters (block 406) and the segment data is modified basedon the input (block 408). By way of example, assume that the look-alikemodel is initially run with a reach goal of 500,000 individuals. Thesegmentation algorithm 202 may be applied based on this reach goal toreturn segment data 208 that places at least 500,000 individuals in thelook-alike segment. Now, if the reach goal is adjusted to 250,000individuals, the next time the segmentation algorithm is run, thereach-accuracy balance is adjusted in favor of accuracy at the expenseof reach. Accordingly, the run of the segmentation algorithm 202 may nowreturn segmentation data 208 with fewer individuals placed into thelook-alike segment but with a relatively higher degree of accuracy. Thisis but one illustrative example of how the ability to change tuningparameters enables a marketer to exert control over look-alike analysisand produce different look-alike segments to meet particular goalsand/or model different scenarios.

Segmentation Algorithms

In the context of the forgoing example environment and procedures,consider now a discussion of some example segmentation algorithms thatmay be employed in at least some implementations. Generally speaking, asuitable segmentation algorithm is configured to score individuals in ageneral audience relative to a target audience definition. Individualsmay be selected for inclusion in the matching, look-alike segment topopulate the segment based on scoring criteria reflected by thesegmentation algorithm and/or tuning parameters employed for theanalysis. For example, a designated number of individuals that score thehighest relative to the model may be assigned to the look-alike segmenton a fixed number basis (e.g., top 100,000 scorers) or a percentagebasis (e.g., top five percent).

In another approach, a threshold score may be employed to determine howto segment the general audience. In this approach, individuals thatachieve the threshold score may be placed into the look-alike segmentwhereas those individuals that do not meet the threshold score are notassigned to the look-alike segment. Scoring criteria such as theexamples just noted may be based on the tuning parameters, such as adesignated reach versus accuracy setting (e.g., reach goal, accuracygoal, reach/accuracy balance, etc.) or other designated goal.

In some implementations, the score is derived as a weighted combinationof traits selected for a model. Traits may be weighted in various waysusing different weight factors, tuning parameters, and algorithms. Tofurther illustrate, consider the following discussion of two examplesegmentation algorithms that may be used in one or more implementations.It should be noted that the enumerated examples are representative ofthe general features of suitable segmentation algorithms describedherein and various other segmentation algorithms are also contemplated.

Example Algorithm 1 Ranking Algorithm

As mentioned in relation to FIG. 2, the inputs to the segmentationalgorithm 202 include the audience data 126, the target audiencedefinition 206 that specifies the target segment and traits to use forthe analysis, and tuning parameters 206 used to selectively control andadjust the manner in which the segmentation algorithm 202 operates. Asalso mentioned, tuning parameters 206 may specify the particularalgorithm to be applied, a reach-accuracy parameter or goal, a timeframe for the analysis (e.g., last 30 days, two weeks, etc.), and soforth.

Using the above mentioned inputs, the first example algorithm computesscores for each individual (e.g., user/client/visitor) based at least inpart upon a relative importance (e.g., weights) assigned to traitsincluded in the target audience definition, e.g., the target segmentthat is going to be expanded through look-alike modeling. The relativeimportance may be determined based upon ratios with which traits occurin the target segment and the general audience, as explained below. Ineffect, the traits are ranked one to another to derive weight factorsfor each trait.

In particular, for the segment or trait(s) to expand (e.g., the targetsegment), the algorithm computes or otherwise obtains the following:

-   -   Traits[ ] //unique traits associated with individuals in the        target segment    -   T_(in) //total number of unique traits in the target segment    -   T_(all) //total number of unique traits in the general audience        per audience data    -   N_(in) //total number of unique individuals in the target        segment    -   N_(all) //total number of unique individuals in the general        audience per audience data

In addition, for each trait in Traits[ ] the algorithm computes orotherwise obtains the following:

-   -   n_(in) //total number of individuals in the target segment        having the trait    -   n_(a1l) //total number of individuals in audience data having        the trait    -   R_(in)=SortedRank(n_(in)/N_(in))/T_(in) //trait importance or        ranking within target segment (normalized rank [0,1])    -   R_(all) SortedRank(n_(a11)/N_(all))/T_(all) //trait importance        or ranking per the audience data (normalized rank [0,1]).

Now, weights based on the normalized trait rankings may be computed asfollows:

-   -   S_(c)=(R_(a11)−R_(in))/R_(in) //trait relative importance        (magnitude, popularity)    -   W_(i)=S_(c)/Sum(S_(c)) //weight factor for trait based on trait        ranks.

Scores may then be computed using the W_(i) values. The scores may becomputed for each individual in the general audience that is not alreadyincluded in the target segment as follows:

Trait existence is defined as:

-   -   t_(i)=[0,1] //1 indicates the user possesses this trait, 0        indicates the user does not possess this trait.

The score for each individual is determined by the following equation:

U _(s)=Sum(W _(i) *t _(i))

In this manner, each individual user may be associated with a score. Thescores may be output as segment data 208 as previously discussed. Thatis, the segment data 208 may indicate a list of unique user identitiesthat are associated with respective scores (U_(s)). Reach versusaccuracy data points and/or graphical representations may be generatedaccording to the segment data 208. Moreover, the scores may be used as abasis to form look-alike segments according to default settings and/orbased on tuning parameters 208 configured by the client/marketer. Forinstance, if a particular reach goal such as 250,000 new users isspecified, the target segment may be expanded by ascertaining and addingthe first 250,000 users associated with the highest respective scores(U_(s)).

Likewise, if a particular accuracy goal such as 0.5 is specified, then adifferent number of users, who satisfy the 0.5 accuracy goal may bereturned for addition to the target segment. It should be noted againthat accuracy indicates how similar of a match exists between thelook-alike individuals and the target segment traits. An accuracy of 1indicates a perfect matching of traits. At an accuracy of 0, all usersin the general audience would be included in the computed segment andthe algorithm would not be considered selective. If the scores U_(s) arenormalized to a scale from 0 to 1, then the accuracy goal represents athreshold score that individuals achieve to be added to the look-alikesegment. In other words, with an accuracy goal set at 0.5, individualswith normalized scores between 0.5 and 1.0 are selected for addition tothe target segment.

Example Algorithm 2 TF-IDF Style Algorithm

The second example segmentation algorithm is a variation of the examplejust described in which trait weights are based on frequency ofoccurrence. This approach is akin to term frequency-inverse documentfrequency (TF-IDF) analysis applied to determine relative importance ofterms in a document, such as for cataloging, keyword generation, datamining and so forth. Here, the frequency of traits within the targetaudience relative to the frequency of the traits in the broader, generalaudience (e.g., audience data) is leveraged to derive weight factors fortraits used for scoring individuals.

Again, for the segment or trait(s) to expand (e.g., the target segment),the algorithm computes or otherwise obtains data indicative of Traits[], T_(in), T_(all), N_(in) and N_(all). Likewise for each trait inTraits[ ] the algorithm also computes or otherwise obtains n_(in) andn_(all). These values are defined in the same ways as discussed inrelation to the foregoing example algorithm.

Now for this variation, rather than using rankings as before, frequencyfactors are computed for each trait as follows:

-   -   TF=% users_(in) having the trait/% users_(all) having the        trait=(n_(in)/N_(in))/(n_(a11)/N_(all)) //this factor represents        the trait frequency    -   IDF=log(N_(all)/n_(all)) //this factor represents the frequency        of the trait within all the database (e.g., audience data).

Now, weights based on the frequency factors may be computed as follows:

-   -   S=TF*IDF //relative frequency scores for traits.    -   W_(i)=S_(c)/Sum(S_(c)) //weight factors based on trait frequency        scores. Note: to simplify the computation a limit may be placed        on the number of traits. For example, a maximum of 1000 traits        may be set. This level may be set to provide adequate results        without overburdening the system.

Similar to the first example algorithm, scores may be computed forindividuals in the general audience using the W, values as follows:

Trait existence is again defined as:

-   -   t_(i)=[0,1] //1 indicates the user possesses this trait, 0        indicates the user does not possess this trait.

The score for each individual is determined by the following equation:

U _(s)=Sum(W _(i) ² *t _(i))

Scores computed in accordance with the second example segmentationalgorithm may be output as segment data 208 and may be used as a basisto form look-alike segments based on the scores in the manner previouslydescribed.

The algorithms just discussed are provided as illustrative examples.Various other algorithms, weights, and scoring techniques may beemployed without departing from the spirit and scope of the describedtechniques.

Example User Interfaces

Consider now a discussion of some example user interfaces that may beemployed to implement aspects of the techniques for tunable algorithmicsegments described herein. The example user interfaces of FIGS. 5-9 maybe provided by a service provider 106 in conjunction with a marketingservice 118. UIs may be arranged in various ways and configured with avariety of functionality/instrumentalities (e.g., buttons, links, menus,input boxes, tabs, icons, lists, graphical representations, selectablecontrols, etc.) to facilitate interaction with a suite of digitalmarketing tools provided by a marketing service 118. A client device 102may access the user interface 120 over a network 108 and rendercorresponding user interfaces 120′ for output at the client. This mayoccur through a standalone marketing module 114, via a browser 116, orotherwise. The user interfaces 120′ may represent a combination of anapplication UI (e.g., a browser frame, app window, or other UI) andpages/content for interaction with the marketing service 118 displayablevia the application UI.

FIG. 5 depicts generally at 500 an example user interface 502 thatenables various interactions with a service provider 106. In thisexample, the user interface is depicted as an interface for a browser116 that presents a web page from the marketing service 118. Otherimplementations are also contemplated, such as user interfacesassociated with a standalone marketing module 114. In the depictedexample, the user interface 502 enables a client/marketer to access anaccount with a service provider 106 and interact with correspondingdata. This includes access to create and manage marketing campaigns,manipulate audience data, produce reports, interact with segments,distribute offers, and so forth.

Here, the user interface 502 includes a menu list 504 that providesdifferent available options to take advantage of digital marketing toolsprovided via the marketing service. For instance, an analytics menu maybe configured with functionality to produce reports, graphs, and/orcustom analysis of audience data. A manage data menu may also beprovided to enable definition and management of traits, segments, andmodels. Other options and instrumentalities are also contemplated, suchas options to access a new model dialog, a help menu, a settings page,and so forth.

In this example, a segments option 506 is illustrated as being selected.The segments option 506 enables a user to navigate existing segments,create new segments, view/modify segments, and so forth. Accordingly, alist of available segments 508 may be retrieved and displayed responsiveto selection of the segments option 506. The page including the list ofavailable segments 508 is configured to facilitate navigation ofdifferent available segments. Options provided within the page mayinclude search functionality to enable searching of databases andfolders to locate segments, controls to create or delete segments, listnavigation controls, and so forth. Comparable pages to navigate traitsand models may also be provided responsive to selection of correspondingoptions from the menu list 504 or operation of another suitable userinterface instrumentality.

As mentioned, a segment corresponds to a group of individuals havingtraits designated for the segment. Each segment may be defined on thebasis of one or multiple selected traits. Different segments areassociated with different lists/groups of user identities that thedigital marketer may employ to selectively provide offers for products,services, and content to individuals in a targeted manner. Some examplesegments represented in the list of FIG. 5 include a camera shoppersegment, a repeat buyer segment, and a mobile device user segment, toname a few. These different segments may be employed in connection withmarketing campaigns to increase the chances that offers are distributedto individuals who are likely to appreciate the offer and/or take actionin response to the offer (e.g., visit a site, buy a product, sign-up fora service, etc.). Segments may be derived in various ways includingthrough application of the tunable algorithmic segment techniquesdescribed above and below.

As represented in FIG. 5, the example user interface 502 also includes acreate model from selected option 510 that facilitates creation of alook-alike model using an existing segment. In this case, a site visitorsegment is illustrated as being selected within the list of availablesegments 508. Operation of the create model from selected option 510with the site visitor segment selected initiates creation of a modelusing the selected segment as the target segment for look-alikeanalysis. In at least some implementations, operation of the createmodel from selected option 510 may cause navigation to another userinterface/page configured with model builder functionality to define alook-alike model based on the selected segment. It should be noted, thata comparable process may be provided to select one or more traitsdefined by the system through a trait navigation interface(s) andinitiate creation of a model based on the selected traits via acorresponding control.

FIG. 6 depicts generally at 600 an example user interface 602 that isrepresentative of a model builder page to facilitate model creation. Theexample model builder page may be output responsive to selection of acreate model control as just described, a selection from the menu list504, or otherwise. The model builder page provides functionality toinput information and make selections to define a model. A basicinformation portion 604 may include fields for input of basic dataregarding the model, such as a name and description. Additional examplesof basic data that may be collected include permissions, keywords, andmodel categorization data to facilitate indexing, searching, andmanagement of the model.

Additionally, the model builder page includes a select target portion606 configured to facilitate selection of existing traits or segments touse as a baseline for the model. A target audience definition 204 may beproduced for the model based upon selections that are made via theselect target portion 608. If the model builder page is accessed via acreate from selected segment or trait control, then the select targetportion 606 may be populated with data regarding the selected segment ortraits. In addition or alternatively, controls operable tonavigate/browse traits and segments may be exposed as depicted in FIG.6. For instance, selection of the browse segments button 608 may causeoutput of and/or navigation to another page, tab, or pop-up window thatincorporates functionality to browse and selected segments. Inparticular, a page comparable to the example user interface 502 of FIG.5 may be output in response to operation of the browse segments button608 as a separate page, in a new tab, as a pop-up/overlay, or otherwise.Navigation to select traits via a browse traits control may occur in alike manner. Responsive to selection of a segment or trait(s) in thisway, the focus may be returned to the model builder page.

The model builder page further provides functionality to set varioustuning parameters 206 for the model. By way of example and notlimitation, the user interface 602 is depicted as including an algorithmselection option 610, a data source selection option 612, and a timeperiod selection option 614. Options to set or modify other tuningparameters, such as reach/accuracy goals and/or an interval forexecution of the model may also be provided in some implementations.

FIG. 7 depicts generally at 700 an example of the user interface 602 ofFIG. 6 following selections made to define the model. Here, a sitevisitor segment has been selected as a basis for the model. Accordinglytarget details 702 are shown for the selected segment. In some cases,the target details 702 may include a graphical representation of thesegment 704, such as an initial reach versus accuracy graph for thetarget. Further, in some scenarios the graphical representation mayinclude controls operable to set or modify tuning parameters for themodel. In one particular example, a reach versus accuracy graphpresented for a model may be operable to set reach or accuracy goals forthe model. Further details of example reach accuracy graphs aredescribed below in relation to FIG. 9.

FIG. 7 further represents a selection 706 of an algorithm “A” fromavailable choices and a selection 708 of one or more data sources foravailable sources, which in this example include both first party data(e.g., the client/marketer's own data) and third-party data availablefrom other providers and services. Individual data sources andcombinations of two or more data sources may be selected. Further, aselection 710 of a time period for the look-alike analysis is depicted.Here, the time period is set to a thirty day look back and accordinglywhen the model is run, data from the selected data sources collected forthe last thirty days will be analyzed for the model using the selectedalgorithm.

FIG. 8 depicts generally at 800 an example of a user interface 802 thatis representative of a model summary view. The example model summaryview may be generated for an existing look-alike model responsive toexecution of the model, on demand, as a report that may be distributedto the client/marketer, or otherwise. In some implementations, a clientmay be notified when the model is run using email or other suitablemessaging techniques. A report having the model summary view may beincluded in the notification and/or a link to access the model summaryview from the marketing service 118 may be provided as part of thenotification.

The model summary view may be configured in various ways to provideinformation regarding and interaction with a corresponding look-alikemodel. For example, a basic information portion 804 may provide detailsregarding the model such as a name, description, tuning parameters, andso forth. In the depicted example, the basic information portion alsoincludes a reach versus accuracy graph as described herein. In someimplementations, the reach versus accuracy graph may be selectable toexpand the graph and/or open a new page for interaction with the graph.This interaction may include reviewing reach/accuracy data as well asmaking changes to tuning parameters for reach and accuracy to adjust themodel. An example interface for adjustment of reach/accuracy is depictedand described in relation to FIG. 9 below.

The example model summary view is further depicted as having a top modeltraits list 806, a processing history portion 808, and a traits usingmodel portion 810 configured to provide further information with respectto aspects of the model. In particular, the top model traits list 806 isconfigured to display selected traits within the model that may have thegreat impact on the model (e.g., the most influential traits). Thisportion also shows the number of unique individuals identified as havingthe listed traits and may rank the traits in the list accordingly. Anoption selectable to view more traits or all traits for the model mayalso be provided.

In addition, the top model traits list 806 may include a check boxcolumn 807 that provides check boxes or other selectable controlsassociated with the listed traits. The check box column 807 isconfigured to enable selection and de-selection of the influentialtraits associated with the model that are listed. This provides thedigital marketer (or other user) with further tunable control over themodel by setting particular traits to be used for the model. Throughselections of traits made via the check box column 807, the digitalmarketer is able to finely tune the model by choosing exactly whichtraits to use for subsequent model runs. Naturally, different traits maybe selected for different runs to test and examine different scenarios.For instance, the digital marketer may have past experience, knowledge,and intuition regarding their audiences that may guide them to choose orremove particular traits or combination of traits returned by theanalysis to tune the model. The check box column 807 provides amechanism by which the digital marketer may inject their experience andintuition into the model to supplemental the algorithmic based numbercrunching power of the model.

The processing history portion 808 is configured to display detailsregarding the processing runs for the model. Individual runs may belisted along with date or time stamps for the runs. The listed runs mayalso be configured as links selectable to access details for a selectedrun. Further, the traits using model portion 810 is configured todisplay selected traits in the system that are built using the model.Reach and accuracy data for the listed traits may be provided inaddition. This portion may also provide functionality to create a newtrait that uses the model, such as via the example button shown in FIG.8. Accordingly, the same model may be applied and reused to createdifferent traits. Tuning parameters, such as reach and accuracy goalsand data sources may be set individually for the different traits. Thus,different traits may be defined for various scenarios to targetdifferent audience members.

FIG. 9 depicts generally at 900 an example of a user interface 902 thatis representative of a page configured to adjust reach and accuracy fora model. The example user interface of FIG. 9 is configured to present adetail view of a reach versus accuracy graph 904 for a model. Inaddition, the underlying segment data generated for the model may bedisplayed in a data details portion 906. Selecting individual pointsfrom the data details portion 906 may cause a callout, animation, orother representation of the selected point to appear in the reach versusaccuracy graph 904. Likewise, selection of points from the graph maycause corresponding points in the data details portion 906 to beemphasized by highlighting, changing text color, or otherwise.

In accordance with techniques described herein, the example userinterface 902 may also include various controls operable to enableadjustments to tuning parameters for the model. Various controls orother instrumentalities may be provided to facilitate changes in tuningparameters and therefore cause corresponding modifications to segmentdata for the model. By way of example and not limitation, a radiocontrol 908 is depicted that is configured to enable selection of areach or accuracy goal for the model. Here, the radio control allows aclient/marketer to select either adjustment by accuracy or adjustment byreach. In the particular example, accuracy is selected and a value of0.08 is set for the accuracy. This selection is represented in the graphby the dashed line corresponding to the 0.08 accuracy goal. Adjustmentby reach may be specified by selection of reach from the radio controland input of a reach goal. Responsive to changes in the selectedvalues/goals, the graph may be updated to reflect the changes. Moreover,the new goal may be applied the next time the model is executed.

In addition or alternatively, other types of controls may be provided inconjunction with the reach versus accuracy graph 904, one example ofwhich is the slider control 910 depicted in FIG. 9. Here, the exampleslider control 910 is operable by sliding left and right to select anaccuracy goal along the horizontal axis of the graph. A comparableslider for adjustments of reach may be displayed along the vertical axisin addition or as an alternative to the accuracy slider control. Variousother types of controls to make adjustment to these and other tuningparameters are also contemplated.

Having considered example user interfaces, consider now a discussion ofan example system and components of the system that can be employed toimplement embodiments of the techniques for tunable algorithmic segmentsdescribed herein.

Example System and Device

FIG. 10 illustrates an example system generally at 1000 that includes anexample computing device 1002 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe marketing service 118, which may be configured to provide a suite ofdigital marketing tools to users of the service. Alternatively, thecomputing device may represent a client device that includes a marketingmodule 114 or browser 116 to implement aspects of the describedtechniques. The computing device 1002 may be, for example, a server of aservice provider, a device associated with a client (e.g., a clientdevice), an on-chip system, and/or any other suitable computing deviceor computing system.

The example computing device 1002 as illustrated includes a processingsystem 1004, one or more computer-readable media 1006, and one or moreI/O interface 1008 that are communicatively coupled, one to another.Although not shown, the computing device 1002 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 1004 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 1004 is illustrated as including hardware element 1010 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 1010 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable media 1006 is illustrated as includingmemory/storage 1012. The memory/storage 1012 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 1012 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 1012 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 1006 may be configured in a variety of otherways as further described below.

Input/output interface(s) 1008 are representative of functionality toallow a user to enter commands and information to computing device 1002,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 1002 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 1002. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable storage of information in contrast to mere signal transmission,carrier waves, or signals per se. Thus, computer-readable storage mediadoes not include signal bearing media or signals per se. Thecomputer-readable storage media includes hardware such as volatile andnon-volatile, removable and non-removable media and/or storage devicesimplemented in a method or technology suitable for storage ofinformation such as computer readable instructions, data structures,program modules, logic elements/circuits, or other data. Examples ofcomputer-readable storage media may include, but are not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,digital versatile disks (DVD) or other optical storage, hard disks,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 1002, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 1010 and computer-readablemedia 1006 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 1010. The computing device 1002 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device1002 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements1010 of the processing system 1004. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 1002 and/or processing systems1004) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 1002 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 1014 via a platform 1016 as describedbelow.

The cloud 1014 includes and/or is representative of a platform 1016 forresources 1018. The platform 1016 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 1014. Theresources 1018 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 1002. Resources 1018 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 1016 may abstract resources and functions to connect thecomputing device 1002 with other computing devices. The platform 1016may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources1018 that are implemented via the platform 1016. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 1000. Forexample, the functionality may be implemented in part on the computingdevice 1002 as well as via the platform 1016 that abstracts thefunctionality of the cloud 1014.

CONCLUSION

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. A method implemented by a computing devicecomprising: obtaining a target audience definition input by a client forcreation of a look-alike model; ascertaining tuning parametersdesignated for the look-alike model; building the look-alike model basedon the target audience definition and the tuning parameters; andexposing segment data generated according to the look-alike model formanipulation by the client.
 2. A method as described in claim 1, furthercomprising: identifying a selection of one or more sources of audiencedata that describes characteristics of a general audience from amongmultiple available sources; and generating the segment data by applyingthe look-alike model to audience data from the identified sources.
 3. Amethod as described in claim 2, wherein: the tuning parameters includean indication of a selection of a segmentation algorithm for thelook-alike model from among multiple available segmentation algorithms;and generating the segment data comprises using the selectedsegmentation algorithm to compute the segment data.
 4. A method asdescribed in claim 3, wherein generating the segment data comprisescomputing scores for each of a plurality of individuals described by theaudience data using the selected segmentation algorithm, the scoresbeing indicative of how closely the individuals match a target segmentof individuals represented by the target audience definition.
 5. Amethod as described in claim 4, wherein the scores are computed as aweighted combination of traits that are indicated by the target audiencedefinition and possessed by individuals described by the audience data.6. A method as described in claim 1, wherein the target audiencedefinition is configured to indicate one or more traits associated witha target segment of consumers who have interacted with resourcesassociated with a digital marketer in a designated manner.
 7. A methodas described in claim 6, wherein the segment data is indicative oflook-alike segments that match the one or more traits of the targetsegment in accordance with the look-alike model.
 8. A method asdescribed in claim 1, wherein exposing the segment data includesproviding one or more options selectable by the client to modify thesegment data by adjusting the tuning parameters and thereby producedifferent corresponding look-alike segments from the segment data.
 9. Amethod as described in claim 1, wherein the tuning parameters include aconfigurable parameter indicative of a reach goal to control a number ofindividual user identities returned in a look-alike segment generatedaccording to the look-alike model.
 10. A method as described in claim 1,wherein the tuning parameters include a configurable parameterindicative of an accuracy goal to control how closely individualsincluded in a look-alike segment generated according to the look-alikemodel match characteristics of a target segment defined by the targetaudience definition.
 11. A method as described in claim 1, furthercomprising: identifying based on the segment data a look-alike segmentof individuals that match selected traits indicated by the targetaudience data; and selectively delivering offers from a digital marketerto the individuals included within the look-alike segment.
 12. One ormore computer-readable storage media comprising instructions that, whenexecuted by a computing device, implement a marketing service configuredto perform operations comprising: outputting a user interface having arepresentation of segment data generated based on analysis of audiencedata from one or more sources according to a look-alike model, thesegment data indicative of individuals in the audience data that matchone or more traits of a target segment defined for the look-alike model;exposing via the user interface one or more controls configured toenable adjustments to tuning parameters associated with the look-alikemodel to modify the segment data; obtaining input via the one or morecontrols to change at least one of the tuning parameters; and modifyingthe segment data based on the input that is obtained.
 13. One or morecomputer-readable storage media of claim 12, wherein the marketingservice is further configured to perform operations to generate thesegment data according to the look-alike model including: obtaining atarget audience definition that defines one or more traits of the targetsegment; ascertaining tuning parameters designated for the look-alikemodel including an indication of a selected segmentation algorithm;determining a selection of the one or more sources of the audience data;and applying the selected segmentation algorithm to compute scores forindividuals described by the audience data indicative of how closely theindividuals match the one or more traits of the target segment.
 14. Oneor more computer-readable storage media of claim 13, wherein themarketing service is further configured to perform operations including:exposing the segment data for access by a client, including providingaccess to a list of user identities for the individuals in associationwith corresponding scores computed for the individuals.
 15. One or morecomputer-readable storage media of claim 12, wherein the representationincludes a reach versus accuracy graph of the segment data.
 16. One ormore computer-readable storage media of claim 12, wherein the one ormore controls include a control configured to specify a reach goal or anaccuracy goal for the analysis of the audience data according to thelook-alike model.
 17. A computing device comprising: one or more modulesimplemented at least partially by hardware of the computing device, theone or more modules configured to perform operations including:obtaining a target audience definition indicative of one or more traitspossessed by a target segment to use for analysis of audience data todiscover a matching segment of individuals in a general audiencepossessing traits similar to the one or more traits of the targetsegment; ascertaining tuning parameters designated for the analysisincluding at least: an indication of a selected segmentation algorithmconfigured to compute scores for individuals in the general audience bya comparison of traits of the individuals in the general audience to theone or more traits possessed by the target segment; and a parameter todesignate a reach versus accuracy setting for the analysis; applying theselected segmentation algorithm to the audience data to compute thescores; and forming the matching segment to achieve the designated reachversus accuracy setting based on the scores.
 18. The computing device ofclaim 17, wherein the matching segment comprises an indication of useridentities for individuals in the general audience having scores thatsatisfy the designated reach versus accuracy setting.
 19. The computingdevice of claim 17, wherein the selected segmentation algorithm isconfigured to generate the scores as a weighted combination of traitspossessed by the individuals using weight factors for the one or moretraits of the target segment that are derived based upon a ranking ofthe traits one to another.
 20. The computing device of claim 17, whereinthe selected segmentation algorithm is configured to generate the scoresas a weighted combination of traits possessed by the individuals usingweight factors for the one or more traits of the target segment that arederived based upon relative frequency with which the traits occur in thetarget segment and the audience data.